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COMPUTING AND APPLYING TRUST IN WEB-BASED SOCIAL NETWORKS

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ABSTRACT

Title of dissertation: COMPUTING AND APPLYING TRUST

IN WEB-BASED SOCIAL NETWORKS

Jennifer Ann Golbeck, Doctor of Philosophy, 2005

Dissertation directed by: Professor James Hendler

Department of Computer Science

The proliferation of web-based social networks has lead to new innovations in

social networking, particularly by allowing users to describe their relationships beyond a

basic connection. In this dissertation, I look specifically at trust in web-based social

networks, how it can be computed, and how it can be used in applications. I begin with a

definition of trust and a description of several properties that affect how it is used in

algorithms. This is complemented by a survey of web-based social networks to gain an

understanding of their scope, the types of relationship information available, and the

current state of trust.

The computational problem of trust is to determine how much one person in the

network should trust another person to whom they are not connected. I present two sets of

algorithms for calculating these trust inferences: one for networks with binary trust

ratings, and one for continuous ratings. For each rating scheme, the algorithms are built

upon the defined notions of trust. Each is then analyzed theoretically and with respect to

simulated and actual trust networks to determine how accurately they calculate the

opinions of people in the system. I show that in both rating schemes the algorithms

presented can be expected to be quite accurate.

These calculations are then put to use in two applications. FilmTrust is a website

that combines trust, social networks, and movie ratings and reviews. Trust is used to

personalize the website for each user, displaying recommended movie ratings, and

ordering reviews by relevance. I show that, in the case where the user's opinion is

divergent from the average, the trust-based recommended ratings are more accurate than

several other common collaborative filtering techniques. The second application is

TrustMail, an email client that uses the trust rating of each sender as a score for the

message. Users can then sort messages according to their trust value.

I conclude with a description of other applications where trust inferences can be

used, and how the lessons from this dissertation can be applied to infer information about

relationships in other complex systems.

COMPUTING AND APPLYING TRUST IN WEB-BASED SOCIAL NETWORKS

by

Jennifer Ann Golbeck

Dissertation Submitted to the Faculty of the Graduate School of the

University of Maryland, College Park in partial fulfillment

of the requirements for the degree of

Doctor of Philosophy

2005

Advisory Committee:

Professor James Hendler, Chair/Advisor

Professor Ashok Agrawala

Professor Mark Austin

Professor Benjamin Bederson

Professor Lise Getoor

Professor Ben Shneiderman

©Copyright by

Jennifer Ann Golbeck

2005

ii

To my parents

iii

ACKNOWLEDGEMENTS

First, I would like to thank James Hendler, my advisor. He gave me great

intellectual freedom to pursue my interests and provided encouragement and guidance

throughout this work’s lifetime. Thanks also to my committee for their challenges,

assistance, and support: Ben Bederson, Ben Shneiderman, Ashok Agrawala, Lise Getoor,

and Mark Austin.

I received what seemed like endless help from members of MINDSWAP in many

capacities. Thanks to Yarden Katz, Mike Grove, Aditya Kalyanpur, Evren Sirin, Ron

Alford, Amy Alford, Debbie Heisler. Aaron Mannes, Denise Cross, and others I may

have forgotten. Special thanks to Bijan Parsia who has been a tireless advocate and

supportive colleague for the life of this work, and who also co-authored the work that

appears as section 10.3. Thanks also to the FOAF community for their support and

participation.

Many colleagues around the world have helped me develop this work into what it

is now. Thanks to Cai-Nicolas Ziegler, Paolo Massa, Matthew Richardson, Morten

Frederiksen, Chris Bizer, and Sep Kamvar. Thanks also to Stuart Kurtz, my former

advisor at the University of Chicago, who helped set me on my way toward this goal.

My family, of course, has been very supportive and encouraging. Thanks to

brother Tom Golbeck and his wife and my friend Michelle, Jeanne Mitchell, and the rest

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